计算机视觉论文-2021-07-29

本专栏是计算机视觉方向论文收集积累，时间：2021年7月29日，来源：paper digest

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1, TITLE: Exceeding The Limits of Visual-Linguistic Multi-Task Learning
AUTHORS: Cameron R. Wolfe ; Keld T. Lundgaard
CATEGORY: cs.AI [cs.AI, cs.CL, cs.CV, cs.LG, 68T07, I.2.6; I.2.7; I.2.10]
HIGHLIGHT: Using our large-scale MTL methodology, we successfully train a single model across all 1000 tasks in our dataset while using minimal task specific parameters, thereby showing that it is possible to extend several orders of magnitude beyond current efforts in MTL.

2, TITLE: A Tale Of Two Long Tails
AUTHORS: Daniel D'souza ; Zach Nussbaum ; Chirag Agarwal ; Sara Hooker
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this work, we seek to identify examples the model is uncertain about and characterize the source of said uncertainty.

3, TITLE: Automated Human Cell Classification in Sparse Datasets Using Few-Shot Learning
AUTHORS: Reece Walsh ; Mohamed H. Abdelpakey ; Mohamed S. Shehata ; Mostafa M. Mohamed
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In practice, a large amount of data is required to accurately train these deep learning models.

4, TITLE: Pseudo-LiDAR Based Road Detection
AUTHORS: Libo Sun ; Haokui Zhang ; Wei Yin
CATEGORY: cs.CV [cs.CV, cs.RO]
HIGHLIGHT: In this paper, we propose a novel road detection approach with RGB being the only input during inference.

5, TITLE: A Novel CropdocNet for Automated Potato Late Blight Disease Detection from The Unmanned Aerial Vehicle-based Hyperspectral Imagery
AUTHORS: Yue Shi ; Liangxiu Han ; Anthony Kleerekoper ; Sheng Chang ; Tongle Hu
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: We have evaluated the proposed method with the real UAV-based HSI data under the controlled field conditions.

6, TITLE: A New Split for Evaluating True Zero-Shot Action Recognition
AUTHORS: Shreyank N Gowda ; Laura Sevilla-Lara ; Kiyoon Kim ; Frank Keller ; Marcus Rohrbach
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a new split for true zero-shot action recognition with no overlap between unseen test classes and training or pre-training classes.

7, TITLE: Aug3D-RPN: Improving Monocular 3D Object Detection By Synthetic Images with Virtual Depth
AUTHORS: Chenhang He ; Jianqiang Huang ; Xian-Sheng Hua ; Lei Zhang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Instead of training a costly depth estimator, we propose a rendering module to augment the training data by synthesizing images with virtual-depths.

8, TITLE: C^3Net: End-to-End Deep Learning for Efficient Real-time Visual Active Camera Control
AUTHORS: Christos Kyrkou
CATEGORY: cs.CV [cs.CV, cs.RO, 65D19, 68T45, 68T07]
HIGHLIGHT: In this paper a deep Convolutional Camera Controller Neural Network is proposed to go directly from visual information to camera movement to provide an efficient solution to the active vision problem.

9, TITLE: Inferring Bias and Uncertainty in Camera Calibration
AUTHORS: Annika Hagemann ; Moritz Knorr ; Holger Janssen ; Christoph Stiller
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we introduce an evaluation scheme to capture the fundamental error sources in camera calibration: systematic errors (biases) and uncertainty (variance).

10, TITLE: Rank-based Verification for Long-term Face Tracking in Crowded Scenes
AUTHORS: Germ�n Barquero ; Isabelle Hupont ; Carles Fern�ndez
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper we present a long-term, multi-face tracking architecture conceived for working in crowded contexts where faces are often the only visible part of a person. We present a series of experiments introducing novel specialized metrics for the evaluation of long-term tracking capabilities, and publicly release a video dataset with 10 manually annotated videos and a total length of 8' 54".

11, TITLE: Unsupervised Segmentation for Terracotta Warrior with Seed-Region-Growing CNN(SRG-Net)
AUTHORS: YAO HU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we present SRG-Net for 3D point clouds of terracotta warriors to address these problems.

12, TITLE: Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
AUTHORS: XIAOFENG LIU et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG, cs.MM]
HIGHLIGHT: In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w.r.t. both $p(x|y)$ and $p(y)$.

13, TITLE: Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
AUTHORS: YANDA MENG et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: This paper proposes a spatial uncertainty-aware semi-supervised approach via regularized surrogate task (binary segmentation) for crowd counting problems.

14, TITLE: Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation
AUTHORS: XIAOFENG LIU et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: Instead of the typically i.i.d. Gaussian latent prior, in this work, a recursively conditional Gaussian (RCG) set is proposed for ordered constraint modeling, which admits a tractable joint distribution prior.

15, TITLE: Accurate Grid Keypoint Learning for Efficient Video Prediction
AUTHORS: Xiaojie Gao ; Yueming Jin ; Qi Dou ; Chi-Wing Fu ; Pheng-Ann Heng
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we design a new grid keypoint learning framework, aiming at a robust and explainable intermediate keypoint representation for long-term efficient video prediction.

16, TITLE: A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision
AUTHORS: TI BAI et. al.
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: In this proof-of-concept study, we demonstrated the concept on 2D axial images of three head-and-neck cancer datasets, with the clinicians input at each iteration being one mouse click on the desired location of the contour segment.

17, TITLE: PlaneTR: Structure-Guided Transformers for 3D Plane Recovery
AUTHORS: Bin Tan ; Nan Xue ; Song Bai ; Tianfu Wu ; Gui-Song Xia
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper presents a neural network built upon Transformers, namely PlaneTR, to simultaneously detect and reconstruct planes from a single image.

18, TITLE: Learning The Shape of Female Breasts: An Open-access 3D Statistical Shape Model of The Female Breast Built from 110 Breast Scans
AUTHORS: Maximilian Weiherer ; Andreas Eigenberger ; Vanessa Br�bant ; Lukas Prantl ; Christoph Palm
CATEGORY: cs.CV [cs.CV, I.4.m; J.3]
HIGHLIGHT: We present the Regensburg Breast Shape Model (RBSM) - a 3D statistical shape model of the female breast built from 110 breast scans, and the first ever publicly available.

19, TITLE: Normalization Matters in Weakly Supervised Object Localization
AUTHORS: Jeesoo Kim ; Junsuk Choe ; Sangdoo Yun ; Nojun Kwak
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we review many existing normalization methods and point out that they should be used according to the property of the given dataset.

20, TITLE: Assessment of Deep Learning-based Heart Rate Estimation Using Remote Photoplethysmography Under Different Illuminations
AUTHORS: Ze Yang ; Haofei Wang ; Feng Lu
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we present a public dataset, namely the BH-rPPG dataset, which contains data from twelve subjects under three illuminations: low, medium, and high illumination.

21, TITLE: MixFaceNets: Extremely Efficient Face Recognition Networks
AUTHORS: Fadi Boutros ; Naser Damer ; Meiling Fang ; Florian Kirchbuchner ; Arjan Kuijper
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we present a set of extremely efficient and high throughput models for accurate face verification, MixFaceNets which are inspired by Mixed Depthwise Convolutional Kernels.

22, TITLE: TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021
AUTHORS: Xiao Gu ; Jianing Qiu ; Yao Guo ; Benny Lo ; Guang-Zhong Yang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this report, the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2021 are given.

23, TITLE: CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation
AUTHORS: Tao Hu ; Chengjiang Long ; Chunxia Xiao
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we focus on the category-consistent and relativistic diverse constraints to optimize the diversity of synthetic images.

24, TITLE: Predicting The Future from First Person (Egocentric) Vision: A Survey
AUTHORS: Ivan Rodin ; Antonino Furnari ; Dimitrios Mavroedis ; Giovanni Maria Farinella
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: The research in egocentric video analysis is developing rapidly thanks to the increasing availability of wearable devices and the opportunities offered by new large-scale egocentric datasets.

25, TITLE: Evaluating The Use of Reconstruction Error for Novelty Localization
AUTHORS: Patrick Feeney ; Michael C. Hughes
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this paper we utilize saliency maps to evaluate whether this correlation exists.

26, TITLE: Unsupervised Monocular Depth Estimation in Highly Complex Environments
AUTHORS: Chaoqiang Zhao ; Yang Tang ; Qiyu Sun
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we investigate the problem of unsupervised monocular depth estimation in certain highly complex scenarios.

27, TITLE: DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis
AUTHORS: Yanchao Yang ; Yuefan Shen ; Youyi Zheng ; C. Karen Liu ; Leonidas Guibas
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We describe a method for realistic depth synthesis that learns diverse variations from the real depth scans and ensures geometric consistency for effective synthetic-to-real transfer.

28, TITLE: Learning-Based Depth and Pose Estimation for Monocular Endoscope with Loss Generalization
AUTHORS: AJI RESINDRA WIDYA et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a novel supervised approach to train depth and pose estimation networks using consecutive endoscopy images to assist the endoscope navigation in the stomach. We firstly generate real depth and pose training data using our previously proposed whole stomach 3D reconstruction pipeline to avoid poor generalization ability between computer-generated (CG) models and real data for the stomach.

29, TITLE: Is Object Detection Necessary for Human-Object Interaction Recognition?
AUTHORS: YING JIN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: This paper revisits human-object interaction (HOI) recognition at image level without using supervisions of object location and human pose.

30, TITLE: Improving Multi-View Stereo Via Super-Resolution
AUTHORS: Eugenio Lomurno ; Andrea Romanoni ; Matteo Matteucci
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we investigate if, how, and how much increasing the resolution of such input images through Super-Resolution techniques reflects in quality improvements of the reconstructed 3D models, despite the artifacts that sometimes this may generate.

31, TITLE: Task-Specific Normalization for Continual Learning of Blind Image Quality Models
AUTHORS: Weixia Zhang ; Kede Ma ; Guangtao Zhai ; Xiaokang Yang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we present a simple yet effective continual learning method for BIQA with improved quality prediction accuracy, plasticity-stability trade-off, and task-order/length robustness.

32, TITLE: Neural Rays for Occlusion-aware Image-based Rendering
AUTHORS: YUAN LIU et. al.
CATEGORY: cs.CV [cs.CV, cs.GR]
HIGHLIGHT: We present a new neural representation, called Neural Ray (NeuRay), for the novel view synthesis (NVS) task with multi-view images as input.

33, TITLE: Surrogate Model-Based Explainability Methods for Point Cloud NNs
AUTHORS: Hanxiao Tan ; Helena Kotthaus
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this paper, we propose new explainability approaches for point cloud deep neural networks based on local surrogate model-based methods to show which components make the main contribution to the classification.

34, TITLE: Subjective Evaluation of Traditional and Learning-based Image Coding Methods
AUTHORS: Zhigao Fang ; Jiaqi Zhang ; Lu Yu ; Yin Zhao
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: We conduct a subjective experiment to compare the performance of traditional image coding methods and learning-based image coding methods.

35, TITLE: DeepTeeth: A Teeth-photo Based Human Authentication System for Mobile and Hand-held Devices
AUTHORS: Geetika Arora ; Rohit K Bharadwaj ; Kamlesh Tiwari
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: This paper proposes teeth-photo, a new biometric modality for human authentication on mobile and hand held devices.

36, TITLE: CarveNet: Carving Point-Block for Complex 3D Shape Completion
AUTHORS: QING GUO et. al.
CATEGORY: cs.CV [cs.CV, cs.GR]
HIGHLIGHT: In this paper, we propose a novel solution,i.e., Point-block Carving (PC), for completing the complex 3D point cloud completion.

37, TITLE: WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification
AUTHORS: Qiufu Li ; Linlin Shen ; Sheng Guo ; Zhihui Lai
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To improve the noise robustness, we try to integrate CNNs with wavelet by replacing the common down-sampling (max-pooling, strided-convolution, and average pooling) with discrete wavelet transform (DWT).

38, TITLE: Improving Video Instance Segmentation Via Temporal Pyramid Routing
AUTHORS: XIANGTAI LI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To incorporate both temporal and scale information, we propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames.

39, TITLE: Shape Controllable Virtual Try-on for Underwear Models
AUTHORS: XIN GAO et. al.
CATEGORY: cs.CV [cs.CV, I.4.9]
HIGHLIGHT: In this paper, we put forward an akin task that aims to dress clothing for underwear models.

40, TITLE: SimROD: A Simple Adaptation Method for Robust Object Detection
AUTHORS: Rindra Ramamonjison ; Amin Banitalebi-Dehkordi ; Xinyu Kang ; Xiaolong Bai ; Yong Zhang
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG, cs.RO]
HIGHLIGHT: This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD).

41, TITLE: Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention
AUTHORS: Min-Cheol Sagong ; Yoon-Jae Yeo ; Seung-Won Jung ; Sung-Jea Ko
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: To solve this problem, in this paper, we propose a novel operation, called pixel adaptive kernel attention (PAKA).

42, TITLE: Image Color Correction, Enhancement, and Editing
AUTHORS: Mahmoud Afifi
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This thesis presents methods and approaches to image color correction, color enhancement, and color editing. Afterward, we discuss another scenario of correcting and editing image colors, where we present a set of methods to correct and edit WB settings for images that have been improperly white-balanced by the ISP.

43, TITLE: Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
AUTHORS: JINLEI HOU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we interpret the reconstruction of an image as a divide-and-assemble procedure.

44, TITLE: A Computer Vision-Based Approach for Driver Distraction Recognition Using Deep Learning and Genetic Algorithm Based Ensemble
AUTHORS: Ashlesha Kumar ; Kuldip Singh Sangwan ; Dhiraj
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: We present an approach using a genetic algorithm-based ensemble of six independent deep neural architectures, namely, AlexNet, VGG-16, EfficientNet B0, Vanilla CNN, Modified DenseNet, and InceptionV3 + BiLSTM.

45, TITLE: A Thorough Review on Recent Deep Learning Methodologies for Image Captioning
AUTHORS: Ahmed Elhagry ; Karima Kadaoui
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We perform in this paper a run-through of the current techniques, datasets, benchmarks and evaluation metrics used in image captioning.

46, TITLE: Global Aggregation Then Local Distribution for Scene Parsing
AUTHORS: XIANGTAI LI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To alleviate this problem, we propose to explore the local context for making the aggregated long-range relationship being distributed more accurately in local regions.

47, TITLE: Multi Point-Voxel Convolution (MPVConv) for Deep Learning on Point Clouds
AUTHORS: WEI ZHOU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Motivated by the success of recent point-voxel representation, such as PVCNN, we propose a new convolutional neural network, called Multi Point-Voxel Convolution (MPVConv), for deep learning on point clouds.

48, TITLE: Experimenting with Self-Supervision Using Rotation Prediction for Image Captioning
AUTHORS: Ahmed Elhagry ; Karima Kadaoui
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this project, we are using an encoder-decoder architecture where the encoder is a convolutional neural network (CNN) trained on OpenImages dataset and learns image features in a self-supervised fashion using the rotation pretext task.

49, TITLE: Graph Constrained Data Representation Learning for Human Motion Segmentation
AUTHORS: Mariella Dimiccoli ; Llu�s Garrido ; Guillem Rodriguez-Corominas ; Herwig Wendt
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Bucking this trend, in this paper, we propose a novel unsupervised model that learns a representation of the data and digs clustering information from the data itself.

50, TITLE: Squeeze-Excitation Convolutional Recurrent Neural Networks for Audio-Visual Scene Classification
AUTHORS: JAVIER NARANJO-ALCAZAR et. al.
CATEGORY: cs.MM [cs.MM, cs.CV, cs.SD, eess.AS, eess.IV]
HIGHLIGHT: This paper presents a multi-modal model for automatic scene classification that exploits simultaneously auditory and visual information.

51, TITLE: A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification
AUTHORS: Uddipan Mukherjee ; Sidharth Pancholi
CATEGORY: eess.AS [eess.AS, cs.CV, cs.SD]
HIGHLIGHT: This research proposes to convert cleansed and normalized heart sound into visual mel scale spectrograms and then using visual domain transfer learning approaches to automatically extract features and categorize between heart sounds.

52, TITLE: An Explainable Two-dimensional Single Model Deep Learning Approach for Alzheimer's Disease Diagnosis and Brain Atrophy Localization
AUTHORS: FAN ZHANG et. al.
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD and localization of important brain regions related to the disease from sMRI data.

53, TITLE: Whole Slide Images Are 2D Point Clouds: Context-Aware Survival Prediction Using Patch-based Graph Convolutional Networks
AUTHORS: RICHARD J. CHEN et. al.
CATEGORY: eess.IV [eess.IV, cs.CV, q-bio.TO]
HIGHLIGHT: In this work, we present Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional network that hierarchically aggregates instance-level histology features to model local- and global-level topological structures in the tumor microenvironment.

54, TITLE: High-speed Object Detection with A Single-photon Time-of-flight Image Sensor
AUTHORS: GERM�N MORA-MART�N et. al.
CATEGORY: eess.IV [eess.IV, cs.CV, physics.optics]
HIGHLIGHT: In this paper, we demonstrate that these limitations can be overcome through the use of convolutional neural networks (CNNs) for high-performance object detection.

55, TITLE: Insights from Generative Modeling for Neural Video Compression
AUTHORS: Ruihan Yang ; Yibo Yang ; Joseph Marino ; Stephan Mandt
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: In particular, we propose (i) improved temporal autoregressive transforms, (ii) improved entropy models with structured and temporal dependencies, and (iii) variable bitrate versions of our algorithms.

56, TITLE: AI Assisted Method for Efficiently Generating Breast Ultrasound Screening Reports
AUTHORS: SHUANG GE et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: Therefore, this paper proposes a method for efficiently generating personalized breast ultrasound screening preliminary reports by AI, especially for benign and normal cases which account for the majority.

57, TITLE: TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
AUTHORS: Madeleine K. Wyburd ; Nicola K. Dinsdale ; Ana I. L. Namburete ; Mark Jenkinson
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this work we propose TEDS-Net: a novel segmentation method that guarantees accurate topology.

58, TITLE: Retinal Microvasculature As Biomarker for Diabetes and Cardiovascular Diseases
AUTHORS: ANUSUA TRIVEDI et. al.
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: Retinal Microvasculature As Biomarker for Diabetes and Cardiovascular Diseases

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